jobd
A self-hostable, GPU-aware job broker for your own machines — with native MCP/agent integration.
Like task-spooler, but across more than one machine — and VRAM-aware.
You have a couple of boxes with GPUs — a workstation, a server, maybe a laptop — wired together over Tailscale or a LAN. You want to fire off training runs, data pipelines, and long batch jobs from anywhere, have them land on whichever machine actually has the VRAM free, survive across sessions, and get preempted cleanly when something more important shows up. You don't have a cloud, a Kubernetes cluster, or a Slurm install, and you don't want one.
jobd is that missing piece: a small broker that turns a handful of personal machines into a single queue — and an LLM agent can drive it directly.
# from any machine on your tailnet:
job submit --project myproj --gpu --vram-required 16 --wait -- python train.py
# → routed to whichever worker has ≥16 GB VRAM free, streamed back to your terminalWhy it exists
Most schedulers assume a datacenter. The lightweight ones that don't (a bare nohup, a tmux session, an ssh-and-pray script) give you nothing: no queue, no VRAM-aware routing, no preemption, no record of what ran where. jobd fills the gap between "ssh in and run it" and "stand up Slurm":
